{"id":"W2726670313","doi":"10.48550/arxiv.1706.08566","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":470,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Banting and Best Diabetes Centre, University of Toronto; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; National Research Foundation; European Commission","keywords":"Computer science; Potential energy surface; Quantum; Grid; Convolutional neural network; Discretization; Deep learning; Chemical space; Filter (signal processing); Invariant (physics); Benchmark (surveying); Quantum dynamics; Differentiable function; Artificial intelligence; Theoretical computer science; Statistical physics; Molecule; Physics; Quantum mechanics; Chemistry; Mathematics; Computer vision; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1020130245770991,"score_gpt":0.2450478718179639,"score_spread":0.1430348472408648,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}